Study on financial risk prediction of enterprises based on logistic regression

2021 ◽  
pp. 1-7
Author(s):  
Qiaoyun Qi
Keyword(s):  
Logistic Regression ◽  
Financial Risk ◽  
Logistic Regression Model ◽  
Great Influence ◽  
Financial Indicators ◽  
Accuracy Rate ◽  
Financial Situation ◽  
Good Market ◽  
A Company

Financial risk has a great influence on the development of enterprises, and the prediction of financial risk can help enterprises to treat risks early. In order to realize the prediction of enterprise financial risk, find the risk as early as possible and make a response as soon as possible, according to the principle of predictability and reliability, this study selected 15 financial indicators from aspects of debt repayment level, profit level, operation level, growth level, and cash level, then predicted the risk by the logistic regression model, and analyzed the risk of 36 pairs of enterprises. The results showed that the model designed in this study had an accuracy rate of 91.67%, and the risk of a company was successfully predicted based on the financial situation of the company from 2018 to 2020, which verified the reliability of the method. Thus the model can effectively predict the financial risk of enterprises, and it can be further promoted and applied to ensure the long-term development of enterprises and establish a good market environment.

scholarly journals The Effect of Internal Financial Indicators on the Tendency of Accounting Fraud

2017 ◽  
Vol 9 (2) ◽  
pp. 123-131
Author(s):  
Dana Iswati ◽  
Marsellisa Nindito ◽  
Adam Zakaria
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Firm Size ◽  
Significant Influence ◽  
Stock Exchange ◽  
Financial Indicators ◽  
Accounting Fraud ◽  
Financial Leverage ◽  
Lemeshow Test ◽  
A Company

This research is carried out to prove factors of tendency of accounting fraud in companies empirically. Variable used in predicting the tendency of accounting fraud is profitability level, capital turnover, financial leverage, assets composition, and firm size tendency of accounting fraud. The population is companies registered in Indonesia Stock Exchange that are suspected to fraud the accounting during observation year 2013 2015. Samples are taken by using purposive sampling, there are 12 companies are proven to be done fraud accounting and 12 companies are not. Data is analysed by using logistic regression analysis and Hosmer and Lemeshow test to measure the model. The result shows that capital turnover and assets composition has significant influence on tendency of accounting fraud. Besides, profitability, financial leverage, and firm size has insignificant influence on tendency of accounting fraud variable. This research concluded that capital turnover and assets composition can be used as predictor of tendency of accounting fraud in a company.

Identification of characteristics associated with long-term survival in patients with metastatic urothelial carcinoma (mUC) who received durvalumab (D) with or without tremelimumab (T) in clinical studies.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 441-441
Author(s):  
Marie Alt ◽  
Carlos Stecca ◽  
Shaum Kabadi ◽  
Benga Kazeem ◽  
Srikala S. Sridhar
Keyword(s):  
Logistic Regression ◽  
Phase I ◽  
Regression Model ◽  
Clinical Studies ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Term Survival ◽  
Long Term Survival

441 Background: Immune checkpoint inhibitors (ICI) have changed the landscape of mUC, yet outcomes are variable as some patients (pts) do not respond to treatment while others have a durable response. To optimally select pts who may derive benefit from ICIs, predictive factors are required. This retrospective, post-hoc analysis evaluated pt characteristics to determine differences between short and long-term survivors among pts with mUC who received D (anti–PD-L1) with or without T (anti–CTLA-4) in two clinical studies. Methods: Pts with platinum-refractory mUC who received D monotherapy in the phase I/II study 1108 (10 mg/kg Q2W, up to 12 mo) or D+T in the phase I study 10 (D at 20 mg/kg + T at 1 mg/kg Q4W for 4 mo, then D at 10 mg/kg Q2W for 12 mo) were included. Pt characteristics, tumor characteristics, radiological assessments, and biological assessments were collected. The primary outcome measure was long-term overall survival (OS). Pts were categorized as OS ≥2 yrs (from 1st dose of study drug) or OS <2 yrs. A univariate analysis was conducted on each baseline characteristic to assess independent associations with long-term OS; a multivariate logistic regression model was employed including each variable with a p-value ≤0.1 as factors or covariates. Results: A total of 367 pts with mUC were included in the analysis: 88 (24.0%) had OS ≥2 yrs (range: 2.09–4.99) and 279 (76.0%) had OS <2 yrs (range: 0.03–1.98). Pts with OS ≥2 yrs had a significantly higher objective response rates than those with OS <2 yrs (71.6% vs 5.7%; p<0.0001) and a significantly longer duration of response (median 2.3 yrs vs 0.39 yrs; p<0.0001). The characteristics included in the multivariate logistic regression model are listed in the Table. Long-term OS was significantly associated with ECOG PS, PD-L1 status, baseline hemoglobin level, and baseline absolute neutrophils count. Conclusions: Our analyses show that several characteristics, including tumor response to treatment, are associated with long-term OS for pts with mUC treated with D or D+T. Further investigation into these and other characteristics may provide additional insights into long-term survival outcomes with ICIs. [Table: see text]

scholarly journals Assessment of factors that affect the bankruptcy of an organization

2020 ◽  
Vol 19 (1) ◽  
pp. 57-71
Author(s):  
Irina Vinnikova
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Timely Manner ◽  
Financial Situation ◽  
Significant Factors

Analysis of factors that influence the company's bankruptcy is one of the main tasks for companies that want to assess their financial situation and prevent possible bankruptcy in a timely manner. This article analyzes the factors that affect the company's bankruptcy. A logistic regression model was constructed based on the indicators of both bankrupt and financially stable companies. During the development of the model, significant factors were identified for predicting the bankruptcy of the organization. The results will be useful both for future bankruptcy researchers and for those companies that want to assess their financial situation.

scholarly journals An examination of accident severity differences between male and female drivers, Using Logistic Regression Model

2015 ◽  
Vol 1 (1) ◽  
pp. 31-36
Author(s):  
Alireza Pakgohar ◽  
Mojtaba Kazemi
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Accuracy Rate ◽  
Model Accuracy ◽  
Male And Female ◽  
Accident Severity ◽  
Probability Of Death

One person in every 2539 people gets killed and one in every 253 suffers injuries due to driving crashes each year in Iran. Such that driving incidents are second rank factor of death and the first rank reason for lost lifetimes in this country. 60% of total incidents which lead to deaths or injuries are actually driving incidents in Iran. That is while the same ratio is only 25% worldwide average. In this article, we report a probabilistic relationship between vehicle drivers’ gender and severity of the accidents. The model accuracy rate is more than 91%. Coefficient values show that if an crash happens and all other variables are under control, the probability of suffering injuries for a man is 1.597 times more than for a woman (1.40 – 1.79, 99% CI) in comparison with the case that the person does not get injured at all. Similarly, the probability of death for a man is 1.462 times higher than for a woman (1.13-1.79, 90% CI) again in comparison with case of no injury at all.

scholarly journals Financial catastrophism inherent with out-of-pocket payments in long term care for households: a latent impoverishment

2019 ◽  
Author(s):  
Raul del Pozo-Rubio ◽  
Isabel Pardo ◽  
Francisco Escribano-Sotos
Keyword(s):  
Household Income ◽  
Financial Risk ◽  
Long Term Care ◽  
Economic Consequences ◽  
Term Care ◽  
Financial Situation ◽  
Dependent Care ◽  
Out Of Pocket Payments ◽  
Before And After

Abstract Background Out-of-pocket (OOP) payments are configured as an important source of financing long-term care (LTC). However, very few studies have analyzed the risk of impoverishment and catastrophic effects of OOP in LTC. Objective To estimate the contribution of users to the financing of LTC and to analyze the economic consequences for households in terms of impoverishment and catastrophism. Data and Methods The data base which was used is 2008 Spanish Disability and Dependency Survey, projected to 2012. We analyze the OOP payments effect associated to the impoverishment of households comparing volume and financial situation before and after OOP payment. At the same time, the extent to which OOP payment had led to catastrophism was analyzed using different thresholds. Results The results show that contribution of dependent people to the financing of the services they receive exceeds by 50% the costs of these services. This expenditure entails an increase in the number of households that live below the poverty. In terms of catastrophism, more than 80% of households dedicate more than 10% of their income to dependency OOP payments. In annual terms, the catastrophe gap generated by devoting more than 10% of the household income to dependent care OOP payment reached €3,955, 1 million (0,38% of GDP). Conclusion This article informs about consequences of OOP in LCT and supplements previous research that focus on health. Our results should serve to develop strategic for protection against the financial risk resulting from facing the costs of a situation of dependence.

scholarly journals Studies regarding the importance of management decisions in ensuring authentic financial sustainability

2020 ◽  
Vol 208 ◽  
pp. 03051
Author(s):  
Gabriela Ignat ◽  
Lilia Șargu ◽  
Haralambie Athes ◽  
Teodor Bivol ◽  
Anelisse Bivol Nigel
Keyword(s):  
Development Strategy ◽  
Financial Risk ◽  
Bank Loans ◽  
Financial Sustainability ◽  
Strong Impact ◽  
Managerial Decisions ◽  
Increased Risk ◽  
Accounting Principle ◽  
A Company

In an economic environment with a perpetually increasing number and complexity of challenges, financial sustainability must be part of the development strategy of any company, emphasizing the good management of the patrimonial resources on the long term. The financial sustainability of a company is actually the ability to generate value with the help of a correct balance between investments and sources of financing. Simultaneously, within a company, the emphasis will be on the vigor of the accounting principle of continuity in long-term operations. Therefore, at the level of each company, managers are faced with challenges related to managerial decisions that would ensure financial sustainability. In Romania most of these companies are financed by bank loans, which leads to a lack of liquidity, which in turn triggers an increased risk of inability to make payments. In a company, financing sources have a strong impact on the liquidity risk, and, therefore, the higher the share of the equity in the company’s total financing sources, the lower the financial risk. The results of our research are relevant for providing useful information in managing the activities of companies. wineries and more. Correct and timely information can help a company to optimize its resources and, in addition, to perform a correct analysis. The aim of our research was to make a diagnosis based on documentation of financial sustainability and the factors that influence it. The case study was conducted at a wine company in Tulcea County, Romania, namely SC Alcovin SRL Măcin.

The prediction algorithm of credit risk of science and technology finance based on cloud computing

2021 ◽  
pp. 1-17
Author(s):  
Guiping Li
Keyword(s):  
Cloud Computing ◽  
Logistic Regression ◽  
Credit Risk ◽  
Regression Model ◽  
Risk Prediction ◽  
Logistic Regression Model ◽  
Science And Technology ◽  
Principal Component ◽  
Prediction Algorithm ◽  
Financial Indicators

In order to effectively guarantee the effect of credit risk prediction of science and technology finance and improve the ability of risk prediction, a credit risk prediction algorithm of science and technology finance based on cloud computing is proposed. The logistic regression model is used to predict, and the financial indicators of science and technology credit are selected as the model covariates. According to the characteristics and strong correlation of many financial indicators of science and technology credit, this paper constructs the final index system of online supply chain technology credit risk evaluation based on SMEs. Then the principal component analysis method is used to select the principal component. Combined with the penalty method, the data space dimension of financial indicators is further reduced, and the unrelated principal components are obtained. On this basis, a logistic regression model is established to predict the credit risk by taking the selected main components as covariates. The experimental results show that the algorithm has a good fit to the credit risk of 16 science and technology credit enterprises, and the risk prediction ability is significantly improved, which can effectively guarantee the effect of science and technology credit risk prediction.

Organisational capabilities and the long-term survival of new technology-based firms

2016 ◽  
Vol 28 (3) ◽  
pp. 312-332 ◽  
Author(s):  
Hans Löfsten
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
New Technology ◽  
Firm Survival ◽  
Term Survival ◽  
Content Type ◽  
Organisational Capabilities ◽  
New Technology Based Firms

Purpose This study aims to analyse organisational capabilities among new technology-based firms (NTBFs) and examine how these capabilities are linked to the firms’ long-term survival. Design/methodology/approach The study leverages a data set of 131 NTBFs located at 16 incubators in Sweden. The first part of the analysis seeks suitable organisational capabilities as determinants of firm survival. The second part is a statistical analysis. The organisational capabilities comprise six variables concerning business experience, financing and international markets. Findings The study comprises two data collections, with the first data collection being conducted in 2005, and the second in 2014. The survival rate for these firms was 55 per cent according to their respective annual reports in 2013. First, this study showed that the logistic regression model that included the three organisational capabilities is significant. Second, one variable is significant at the variable level: business experience. In addition, the control variable firm size is also significant. Originality/value Further empirical research in this area is required as the current research on organisational capabilities is quite limited and mainly conceptual in nature. Very few related studies focus on NTBFs and their survival. This study demonstrates a significant logistic regression model to determine links between organisational capabilities and firm survival.

scholarly journals Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe

2020 ◽  
Vol 12 (13) ◽  
pp. 5317 ◽  
Author(s):  
Caterina De Lucia ◽  
Pasquale Pazienza ◽  
Mark Bartlett
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Investment Decisions ◽  
Current Debate ◽  
Financial Indicators ◽  
Public Enterprises ◽  
Responsible Investment ◽  
Ordered Logistic Regression

The increasing awareness of climate change and human capital issues is shifting companies towards aspects other than traditional financial earnings. In particular, the changing behaviors towards sustainability issues of the global community and the availability of environmental, social and governance (ESG) indicators are attracting investors to socially responsible investment decisions. Furthermore, whereas the strategic importance of ESG metrics has been particularly studied for private enterprises, little attention have received public companies. To address this gap, the present work has three aims—1. To predict the accuracy of main financial indicators such as the expected Return of Equity (ROE) and Return of Assets (ROA) of public enterprises in Europe based on ESG indicators and other economic metrics; 2. To identify whether ESG initiatives affect the financial performance of public European enterprises; and 3. To discuss how ESG factors, based on the findings of aims #1 and #2, can contribute to the advancements of the current debate on Corporate Social Responsibility (CSR) policies and practices in public enterprises in Europe. To fulfil the above aims, we use a combined approach of machine learning (ML) techniques and inferential (i.e., ordered logistic regression) model. The former predicts the accuracy of ROE and ROA on several ESG and other economic metrics and fulfils aim #1. The latter is used to test whether any causal relationships between ESG investment decisions and ROA and ROE exist and, whether these relationships exist, to assess their magnitude. The inferential analysis fulfils aim #2. Main findings suggest that ML accurately predicts ROA and ROE and indicate, through the ordered logistic regression model, the existence of a positive relationship between ESG practices and the financial indicators. In addition, the existing relationship appears more evident when companies invest in environmental innovation, employment productivity and diversity and equal opportunity policies. As a result, to fulfil aim #3 useful policy insights are advised on these issues to strengthen CSR strategies and sustainable development practices in European public enterprises.

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